Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Dr. Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Jinling Institute of Technology | China

Dr. Yuying Chen is a dedicated materials scientist in the Department of Materials Engineering at the School of Materials Engineering, Jinling Institute of Technology, Nanjing, China, where she contributes extensively to research, teaching, and the advancement of materials innovation. She earned her Ph.D. in Materials Science from the Harbin Institute of Technology and enriched her international academic profile through a visiting Ph.D. appointment at the Department of Mining and Materials Engineering at McGill University in Montreal, Canada. Her academic background also includes a Master’s degree in Materials Science from the Harbin Institute of Technology and a Bachelor’s degree in Metal Materials Engineering from Shenyang University of Technology. Dr. Chen’s research expertise encompasses first-principles calculations, hydrogen storage materials, interface engineering, alloying effects, metal hydrides, and computational modeling of welding processes. She has authored 8 documents that investigate hydrogen adsorption and desorption mechanisms, Mg/Ni and Mg/Ti interface stability, alkali- and alkaline-earth-metal-doped hydrides, Zn-induced embrittlement behavior in steels, and advanced modeling techniques for underwater wet welding and duplex stainless-steel welding under acoustic and vibrational fields. Her scholarly contributions have accumulated 90 citations and reflect an impactful research profile with an h-index of 5, demonstrating the academic significance and visibility of her work within the materials science community. Over the course of her academic journey, Dr. Chen has received numerous accolades, including Merit Student awards, multiple University Fellowships, Outstanding Student Leader recognition, and acknowledgment as an Excellent League Member at Harbin Institute of Technology. She has presented her research findings at major scientific gatherings, including the International Conference on Computational Design and Simulation of Materials and the Chinese Materials Conference. With a strong record in computational materials science and interface behavior, Dr. Chen continues to advance innovative methodologies and scientific understanding toward the design, optimization, and reliability of next-generation materials systems.

Profiles: Scopus Orcid

Featured Publications

Chen, Y., Dai, J., & Song, Y. Catalytic mechanisms of TiH2 thin layer on dehydrogenation behavior of fluorite-type MgH2: A first principles study.

Chen, Y. Y., Dai, J. H., Xie, R. W., & Song, Y. A first-principles study on interaction of Mg/Ni interface and its hydrogen absorption characteristics.

Chen, Y. Y., Dai, J. H., Xie, R. W., Song, Y., & Bououdina, M. First principles study of dehydrogenation properties of alkali and alkali-earth metal doped Mg₇TiH₁₆.

Chen, Y. Y., Dai, J. H., & Song, Y. Stability and hydrogen adsorption properties of Mg/Mg₂Ni interface: A first principles study.

Dai, J. H., Chen, Y. Y., Xie, R. W., & Song, Y. Influence of alloying elements on the stability and dehydrogenation properties of Y(BH₄)₃ by first principles calculations.

Moumita Mukherjee | Machine Learning and Statistics | Best Researcher Award

Dr. Moumita Mukherjee | Machine Learning and Statistics | Best Researcher Award

Charite-University Medicine Berlin | Germany

Dr. Moumita Mukherjee is an accomplished health economist and digital health researcher with expertise in health systems research, machine learning applications in healthcare, and interdisciplinary teaching. She holds a PhD in Economics from the University of Calcutta, an MBA in Entrepreneurship, Innovation and Project Development from International Telematic University, and an MSc in Data Science from the University of Europe for Applied Sciences, Germany. Her professional experience spans both academic and applied research environments, including positions at Charite-University Medicine Berlin, the Indian Institute of Public Health in Shillong, and the Berlin School of Business and Innovation. She has contributed extensively to global health research focusing on digital transformation, equity in healthcare access, and the use of data-driven methods for improving health outcomes. Her body of work includes numerous peer-reviewed publications in leading journals such as Scientific Reports, Journal of Health, Population and Nutrition, Journal of Health Management, and International Journal for Equity in Health, as well as book chapters and authored volumes addressing child health, nutrition, and health equity. In her current role at Charite-University Medicine Berlin, she lectures on digital health and artificial intelligence, supervises master’s theses, and mentors students. With advanced technical proficiency in Python, STATA, and NVivo, she applies econometric, machine learning, and deep learning models to address complex public health and policy questions. Her interdisciplinary approach integrates health economics, digital innovation, and policy analysis to support equitable and sustainable health systems worldwide. Through her research, teaching, and mentorship, Dr. Moumita Mukherjee continues to bridge data science and health economics to shape the future of evidence-based global health policy and digital healthcare transformation.

Profiles: Google Scholar | Orcid

Featured Publications